Ontology highlight
ABSTRACT: Aims
Gestational diabetes mellitus (GDM) poses a significant risk for developing type 2 diabetes mellitus (T2D) and exhibits heterogeneity. However, understanding the link between different types of post-GDM individuals without diabetes and their progression to T2D is crucial to advance personalised medicine approaches.Materials and methods
We employed a discovery-based unsupervised machine learning clustering method to generate clustering models for analysing metabolomics, clinical, and biochemical datasets. For this analysis, we selected 225 women who later developed T2D during the 12-year follow-up period from the cohort of 1010 women who returned to a non-diabetic state at 6-9 weeks (study baseline) after a GDM pregnancy based on 2-h 75 g research OGTTs. The optimal mod
SUBMITTER: Khan SR
PROVIDER: S-EPMC11733828 | biostudies-literature | 2025 Jan
REPOSITORIES: biostudies-literature